Data Science Workspace overview

The vision of machine learning on Adobe Experience Platform is to democratize data science by using the domain expertise of Adobe products, customers, and partners to create an ecosystem of intelligent services to power the next generation of customer experiences. Data Science Workspace makes it easy to access omni-channel data, build models, operationalize models with a one-click deployment, and consume model insights by sharing them via real-time customer profiles. This video gives an overview of what Data Science Workspace is and the value it provides to businesses. For more information, please visit the Data Science Workspace documentation.

In this video, we are going to introduce you to Data Science Workspace in Adobe Experience Platform. Data science permeates Adobe products. For marketers and analysts, we provide AI and ML features directly in the Experience Cloud applications as well as AI as a service in our intelligence services such as customer AI and attribution AI. For data scientists, we have Data Science Workspace which was designed to streamline and simplify the data science workflow from data gathering to algorithms. All will significantly shortening the time it takes to get from raw data to actionable insights. Data Science Workspace lets you easily bring in existing models into activation workflows or build entirely new ones. Data science teams can directly integrate their AI and ML investments with Experience Platforms, Real-Time Customer profile. Let’s take a look at some common use cases. One being AI enabled segment discovery. Who are the key visitors to your site? Which prospects are likely to convert? And which existing customers are likely to churn? Experience Platform provides an end-to-end machine learning framework to develop models to help drive segmentation on your Real-Time Customer profile data. This is done through scheduled training and scoring jobs to generate insights as new input and training data becomes available. Another scenario is machine learning driven omni-channel experiences such as web and app personalization. With Real-Time Customer profiles, Experience Cloud data and any custom data, you can easily develop your machine learning models to derive insights for actioning.
Although this may sound straightforward, implementing this process comes with a set of challenges. Existing systems were not built to support continuous insights and intelligence. Data is everywhere and siloed leading to higher latencies. Data preparation is slow and tedious. As we all know, a lot of a data scientist’s time is spent on data preparation instead of modeling. Systems are not designed for real time. Decisions are locked away in specific applications across the enterprise. Privacy and policies are constantly changing making it difficult to adapt. And insights and intelligence are not connected to engagement systems.
As a business analyst or data scientist, overcoming these challenges to drive personalized experiences for your business is extremely difficult.
With that in mind, let’s dive deeper into what the machine learning and artificial intelligence journey looks like in Data Science Workspace and look at how we can solve these machine learning challenges. The journey starts with data preparation. A data scientist should have easy access to all omni-channel data and shouldn’t have to spend too much time preparing for insights. With Jupyter Lab Notebooks natively integrated with Platform, accessing this data is only a few clicks away. To assist with data preparation and data analysis, data scientists can leverage Real-Time Customer profile. Profile gives you a holistic view of each individual customer by combining data from multiple channels including online, offline, CRM, and third parties allowing you to consolidate your customer data into a unified view, offering an actionable, timestamped account of every customer interaction. This gives data scientists full access to stitched and raw attribute as well as event data in an optimized format. Once data preparation has taken place, we move to model development. This is the core task of a data scientist, and where most of their time should be spent. To do this effectively, a data scientist is able to use state-of-the-art technologies to build models. Bringing existing code or use pre-built Adobe models with either the Platform interface or Adobe Sensei API. Like the rest of Platform, Data Science Workspace was built with an API first approach. The data available in Platform can be accessed through Data Science Workspace using query service reducing the need to clean, scrub, and fix incoming data. Since Adobe’s XDM Experience Data Model has already standardized all Platform schemas to reduce latency and data collection. Next, Data Science Workspace provides the ability to train and evaluate models directly within Platform. The machine learning workflow helps to rapidly experiment with different model configurations against any of your data sets small or large in the data lake. After developing a model, a data scientist can easily activate the model by deploying it as a service. Once a service is deployed, you’re able to monitor it using Data Science Workspace’s ML service management. To ensure that the output is being generated as expected and written back to the data lake. This enables continuous learning and retraining of the models to improve your predictions over time. Your predictions can then be executed through batch and real-time jobs to enrich your profile data. These predictions, machine learning, and artificial intelligence insights, can easily be activated to both Adobe and non-Adobe products by your marketing team leaving customers with more personalized experiences. In other words, Data Science Workspace provides one seamless workflow that allows you to go from data to consuming your insights all within Experience Platform.
When looking at the customer journey from how a customer discovers your brand, how they try your products or services, buy and use those products, and any engagement thereafter. AI can play a key role across each phase of the customer journey. Whether you want to curate content targeted to customers or calculate propensity of conversions. Having an AI Playbook across the customer journey is key to continuously engage with your customers in meaningful ways to build long-term brand loyalty. With Data Science Workspace, data science can now happen as close as possible to the origin of the data. In addition, data scientists can spend most of their time on predictions and insights, while marketers can easily activate the outputs of a data scientists’ efforts in both Adobe and non-Adobe products. You should now have a sense of what Data Science Workspace is and how your business can leverage Data Science Workspace. Thanks for watching. - -